Abstract

Text labels in raster maps provide valuable geospatial information by associating geographical names with geospatial locations. Although present commercial optical character recognition (OCR) products can achieve a high recognition rate on documents containing text lines of the same orientation, text recognition on raster maps is challenging due to the varying text orientations and the overlap of text labels. This paper presents a text recognition approach that focuses on locating individual text labels in the map and detecting their orientations to then leverage the horizontal text recognition capability of commercial OCR software. We show that our approach detects accurate string orientations and achieves 96.2% precision and 94.7% recall on character recognition and 80.6% precision and 84.1% recall on word recognition.

Date

August 23, 2010

Authors

Yao-Yi Chiang, Craig A. Knoblock

Conference

Pattern Recognition (ICPR), 2010 20th International Conference on

Pages

3199-3202

Publisher

IEEE